{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> NSL-KDD Dataset Preprocessing Notebook <center/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas_profiling, pickle\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from preprocessing import get_data\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler,power_transform\n",
    "from scipy.stats import skew"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data and do Profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test, label_mapping = get_data()\n",
    "\n",
    "profile = pandas_profiling.ProfileReport(train)\n",
    "profile.to_file(outputfile=\"NSLKDD profile report.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove rejected features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>same_srv_rate</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>150</td>\n",
       "      <td>25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>146</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255</td>\n",
       "      <td>26</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>232</td>\n",
       "      <td>8153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>199</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  protocol_type  service  flag  src_bytes  dst_bytes  land  \\\n",
       "0         0              1        1     1        491          0     0   \n",
       "1         0              2        2     1        146          0     0   \n",
       "2         0              1        3     2          0          0     0   \n",
       "3         0              1        4     1        232       8153     0   \n",
       "4         0              1        4     1        199        420     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...    same_srv_rate  diff_srv_rate  \\\n",
       "0               0       0    0  ...             1.00           0.00   \n",
       "1               0       0    0  ...             0.08           0.15   \n",
       "2               0       0    0  ...             0.05           0.07   \n",
       "3               0       0    0  ...             1.00           0.00   \n",
       "4               0       0    0  ...             1.00           0.00   \n",
       "\n",
       "   srv_diff_host_rate  dst_host_count  dst_host_srv_count  \\\n",
       "0                0.00             150                  25   \n",
       "1                0.00             255                   1   \n",
       "2                0.00             255                  26   \n",
       "3                0.00              30                 255   \n",
       "4                0.09             255                 255   \n",
       "\n",
       "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                    0.17                    0.03   \n",
       "1                    0.00                    0.60   \n",
       "2                    0.10                    0.05   \n",
       "3                    1.00                    0.00   \n",
       "4                    1.00                    0.00   \n",
       "\n",
       "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  label  \n",
       "0                         0.17                         0.00      1  \n",
       "1                         0.88                         0.00      1  \n",
       "2                         0.00                         0.00      0  \n",
       "3                         0.03                         0.04      1  \n",
       "4                         0.00                         0.00      1  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_remove = profile.get_rejected_variables()\n",
    "train.drop(to_remove, axis=1, inplace=True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data ,y_train = train.drop([\"label\"],axis=1),train.label.values\n",
    "\n",
    "label_cols = ['label']\n",
    "data_cols = list(data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <center> Scale Data <center/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Robust Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.51</td>\n",
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       "      <td>-0.016129</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>-0.000020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.145669</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.035093</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>-0.885827</td>\n",
       "      <td>0.755906</td>\n",
       "      <td>0.49</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
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      "text/plain": [
       "   duration  protocol_type   service      flag  src_bytes  dst_bytes  land  \\\n",
       "0       0.0            0.0 -0.048387  0.000000   0.000204   0.000000   0.0   \n",
       "1       0.0            0.5 -0.032258  0.000000   0.000046   0.000000   0.0   \n",
       "2       0.0            0.0 -0.016129  0.111111  -0.000020   0.000000   0.0   \n",
       "3       0.0            0.0  0.000000  0.000000   0.000086   0.035093   0.0   \n",
       "4       0.0            0.0  0.000000  0.000000   0.000071   0.001808   0.0   \n",
       "\n",
       "   wrong_fragment  urgent  hot             ...               rerror_rate  \\\n",
       "0             0.0     0.0  0.0             ...                       0.0   \n",
       "1             0.0     0.0  0.0             ...                       0.0   \n",
       "2             0.0     0.0  0.0             ...                       0.0   \n",
       "3             0.0     0.0  0.0             ...                       0.0   \n",
       "4             0.0     0.0  0.0             ...                       0.0   \n",
       "\n",
       "   same_srv_rate  diff_srv_rate  srv_diff_host_rate  dst_host_count  \\\n",
       "0           0.00           0.00                0.00       -0.413386   \n",
       "1          -0.92           0.15                0.00        0.000000   \n",
       "2          -0.95           0.07                0.00        0.000000   \n",
       "3           0.00           0.00                0.00       -0.885827   \n",
       "4           0.00           0.00                0.09        0.000000   \n",
       "\n",
       "   dst_host_srv_count  dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0           -0.149606                   -0.34                    0.01   \n",
       "1           -0.244094                   -0.51                    0.58   \n",
       "2           -0.145669                   -0.41                    0.03   \n",
       "3            0.755906                    0.49                   -0.02   \n",
       "4            0.755906                    0.49                   -0.02   \n",
       "\n",
       "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \n",
       "0                         0.17                         0.00  \n",
       "1                         0.88                         0.00  \n",
       "2                         0.00                         0.00  \n",
       "3                         0.03                         0.04  \n",
       "4                         0.00                         0.00  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_Robust_trans = pd.DataFrame(data=RobustScaler(quantile_range=(0.1, 99.9 )).fit_transform(data[data_cols].values),columns=list(data.columns))\n",
    "data_Robust_trans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.007185</td>\n",
       "      <td>0.125324</td>\n",
       "      <td>0.114631</td>\n",
       "      <td>0.069133</td>\n",
       "      <td>0.020743</td>\n",
       "      <td>0.085135</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.007562</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.006814</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119958</td>\n",
       "      <td>-0.339072</td>\n",
       "      <td>0.063053</td>\n",
       "      <td>0.097322</td>\n",
       "      <td>-0.286815</td>\n",
       "      <td>0.207295</td>\n",
       "      <td>0.011242</td>\n",
       "      <td>0.062951</td>\n",
       "      <td>0.148379</td>\n",
       "      <td>0.032542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.065171</td>\n",
       "      <td>0.282603</td>\n",
       "      <td>0.205811</td>\n",
       "      <td>0.113911</td>\n",
       "      <td>2.674875</td>\n",
       "      <td>17.308644</td>\n",
       "      <td>0.014086</td>\n",
       "      <td>0.084510</td>\n",
       "      <td>0.014366</td>\n",
       "      <td>0.071666</td>\n",
       "      <td>...</td>\n",
       "      <td>0.320436</td>\n",
       "      <td>0.439623</td>\n",
       "      <td>0.180314</td>\n",
       "      <td>0.259830</td>\n",
       "      <td>0.390576</td>\n",
       "      <td>0.435838</td>\n",
       "      <td>0.448949</td>\n",
       "      <td>0.188922</td>\n",
       "      <td>0.308997</td>\n",
       "      <td>0.112564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.048387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.003937</td>\n",
       "      <td>-0.248031</td>\n",
       "      <td>-0.510000</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.016129</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.910000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.681102</td>\n",
       "      <td>-0.208661</td>\n",
       "      <td>-0.460000</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.177419</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.002221</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.755906</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.073651</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.064516</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>628.794266</td>\n",
       "      <td>5638.329329</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.566667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.755906</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            duration  protocol_type        service           flag  \\\n",
       "count  125973.000000  125973.000000  125973.000000  125973.000000   \n",
       "mean        0.007185       0.125324       0.114631       0.069133   \n",
       "std         0.065171       0.282603       0.205811       0.113911   \n",
       "min         0.000000       0.000000      -0.048387       0.000000   \n",
       "25%         0.000000       0.000000      -0.016129       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.177419       0.111111   \n",
       "max         1.073651       1.000000       1.064516       1.111111   \n",
       "\n",
       "           src_bytes      dst_bytes           land  wrong_fragment  \\\n",
       "count  125973.000000  125973.000000  125973.000000   125973.000000   \n",
       "mean        0.020743       0.085135       0.000198        0.007562   \n",
       "std         2.674875      17.308644       0.014086        0.084510   \n",
       "min        -0.000020       0.000000       0.000000        0.000000   \n",
       "25%        -0.000020       0.000000       0.000000        0.000000   \n",
       "50%         0.000000       0.000000       0.000000        0.000000   \n",
       "75%         0.000106       0.002221       0.000000        0.000000   \n",
       "max       628.794266    5638.329329       1.000000        1.000000   \n",
       "\n",
       "              urgent            hot             ...               \\\n",
       "count  125973.000000  125973.000000             ...                \n",
       "mean        0.000111       0.006814             ...                \n",
       "std         0.014366       0.071666             ...                \n",
       "min         0.000000       0.000000             ...                \n",
       "25%         0.000000       0.000000             ...                \n",
       "50%         0.000000       0.000000             ...                \n",
       "75%         0.000000       0.000000             ...                \n",
       "max         3.000000       2.566667             ...                \n",
       "\n",
       "         rerror_rate  same_srv_rate  diff_srv_rate  srv_diff_host_rate  \\\n",
       "count  125973.000000  125973.000000  125973.000000       125973.000000   \n",
       "mean        0.119958      -0.339072       0.063053            0.097322   \n",
       "std         0.320436       0.439623       0.180314            0.259830   \n",
       "min         0.000000      -1.000000       0.000000            0.000000   \n",
       "25%         0.000000      -0.910000       0.000000            0.000000   \n",
       "50%         0.000000       0.000000       0.000000            0.000000   \n",
       "75%         0.000000       0.000000       0.060000            0.000000   \n",
       "max         1.000000       0.000000       1.000000            1.000000   \n",
       "\n",
       "       dst_host_count  dst_host_srv_count  dst_host_same_srv_rate  \\\n",
       "count   125973.000000       125973.000000           125973.000000   \n",
       "mean        -0.286815            0.207295                0.011242   \n",
       "std          0.390576            0.435838                0.448949   \n",
       "min         -1.003937           -0.248031               -0.510000   \n",
       "25%         -0.681102           -0.208661               -0.460000   \n",
       "50%          0.000000            0.000000                0.000000   \n",
       "75%          0.000000            0.755906                0.490000   \n",
       "max          0.000000            0.755906                0.490000   \n",
       "\n",
       "       dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "count           125973.000000                125973.000000   \n",
       "mean                 0.062951                     0.148379   \n",
       "std                  0.188922                     0.308997   \n",
       "min                 -0.020000                     0.000000   \n",
       "25%                 -0.020000                     0.000000   \n",
       "50%                  0.000000                     0.000000   \n",
       "75%                  0.050000                     0.060000   \n",
       "max                  0.980000                     1.000000   \n",
       "\n",
       "       dst_host_srv_diff_host_rate  \n",
       "count                125973.000000  \n",
       "mean                      0.032542  \n",
       "std                       0.112564  \n",
       "min                       0.000000  \n",
       "25%                       0.000000  \n",
       "50%                       0.000000  \n",
       "75%                       0.020000  \n",
       "max                       1.000000  \n",
       "\n",
       "[8 rows x 34 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_Robust_trans.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Plot_data_features(data_cols, X, y = None):\n",
    "    axarr = [[]]*len(data_cols)\n",
    "    columns = 4\n",
    "    rows = int( np.ceil( len(data_cols) / columns ) )\n",
    "    f, fig = plt.subplots( figsize=(columns*3.5, rows*2) )\n",
    "\n",
    "    f.suptitle('Data Distributions by Feature and Class', size=16)\n",
    "\n",
    "    for i, col in enumerate(data_cols[:]):\n",
    "        axarr[i] = plt.subplot2grid( (int(rows), int(columns)), (int(i//columns), int(i%columns)) )\n",
    "        axarr[i].hist( [ X.loc[ y == 1, col ], X.loc[ y != 1, col ] ], label=['Normal','Attack'], \n",
    "                              bins=np.linspace( np.percentile(X[col],0.1), np.percentile(X[col],99.9), 30 ),\n",
    "                              normed=True )\n",
    "        axarr[i].set_xlabel(col, size=12)\n",
    "        axarr[i].set_ylim([0,0.8])\n",
    "        axarr[i].tick_params(axis='both', labelsize=10)\n",
    "        if i == 0: \n",
    "            legend = axarr[i].legend()\n",
    "            legend.get_frame().set_facecolor('white')\n",
    "        if i%4 != 0 : \n",
    "            axarr[i].tick_params(axis='y', left='off', labelleft='off')\n",
    "        else:\n",
    "            axarr[i].set_ylabel('Fraction',size=12)\n",
    "\n",
    "    plt.tight_layout(rect=[0,0,1,0.95]) \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x1800 with 34 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_data_features(X=data_Robust_trans,data_cols=data_cols,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save data\n",
    "data_Robust_trans[\"Class\"] = y_train\n",
    "pickle.dump(data_Robust_trans, open('../Data/NSL-KDD/data_Robust_trans.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Power Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>rerror_rate</th>\n",
       "      <th>same_srv_rate</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.293467</td>\n",
       "      <td>-0.476159</td>\n",
       "      <td>-2.046509</td>\n",
       "      <td>-0.811815</td>\n",
       "      <td>1.012501</td>\n",
       "      <td>-0.900230</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.093425</td>\n",
       "      <td>-0.008453</td>\n",
       "      <td>-0.147181</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.381289</td>\n",
       "      <td>0.778461</td>\n",
       "      <td>-0.746146</td>\n",
       "      <td>-0.496460</td>\n",
       "      <td>-0.309308</td>\n",
       "      <td>-0.479823</td>\n",
       "      <td>-0.728866</td>\n",
       "      <td>-0.106055</td>\n",
       "      <td>1.249816</td>\n",
       "      <td>-0.591798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.293467</td>\n",
       "      <td>2.085784</td>\n",
       "      <td>-1.269036</td>\n",
       "      <td>-0.811815</td>\n",
       "      <td>0.654220</td>\n",
       "      <td>-0.900230</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.093425</td>\n",
       "      <td>-0.008453</td>\n",
       "      <td>-0.147181</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.381289</td>\n",
       "      <td>-1.320214</td>\n",
       "      <td>1.887607</td>\n",
       "      <td>-0.496460</td>\n",
       "      <td>0.732270</td>\n",
       "      <td>-1.589673</td>\n",
       "      <td>-1.203503</td>\n",
       "      <td>2.308407</td>\n",
       "      <td>2.014334</td>\n",
       "      <td>-0.591798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.293467</td>\n",
       "      <td>-0.476159</td>\n",
       "      <td>-0.787101</td>\n",
       "      <td>1.007423</td>\n",
       "      <td>-1.119311</td>\n",
       "      <td>-0.900230</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.093425</td>\n",
       "      <td>-0.008453</td>\n",
       "      <td>-0.147181</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.381289</td>\n",
       "      <td>-1.356394</td>\n",
       "      <td>1.068041</td>\n",
       "      <td>-0.496460</td>\n",
       "      <td>0.732270</td>\n",
       "      <td>-0.459048</td>\n",
       "      <td>-0.917737</td>\n",
       "      <td>0.332260</td>\n",
       "      <td>-0.670457</td>\n",
       "      <td>-0.591798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.293467</td>\n",
       "      <td>-0.476159</td>\n",
       "      <td>-0.448643</td>\n",
       "      <td>-0.811815</td>\n",
       "      <td>0.793761</td>\n",
       "      <td>1.454865</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.093425</td>\n",
       "      <td>-0.008453</td>\n",
       "      <td>-0.147181</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.381289</td>\n",
       "      <td>0.778461</td>\n",
       "      <td>-0.746146</td>\n",
       "      <td>-0.496460</td>\n",
       "      <td>-1.531294</td>\n",
       "      <td>1.096817</td>\n",
       "      <td>1.049686</td>\n",
       "      <td>-0.973971</td>\n",
       "      <td>-0.108002</td>\n",
       "      <td>1.446408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.293467</td>\n",
       "      <td>-0.476159</td>\n",
       "      <td>-0.448643</td>\n",
       "      <td>-0.811815</td>\n",
       "      <td>0.747902</td>\n",
       "      <td>0.968808</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.093425</td>\n",
       "      <td>-0.008453</td>\n",
       "      <td>-0.147181</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.381289</td>\n",
       "      <td>0.778461</td>\n",
       "      <td>-0.746146</td>\n",
       "      <td>1.307375</td>\n",
       "      <td>0.732270</td>\n",
       "      <td>1.096817</td>\n",
       "      <td>1.049686</td>\n",
       "      <td>-0.973971</td>\n",
       "      <td>-0.670457</td>\n",
       "      <td>-0.591798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  protocol_type   service      flag  src_bytes  dst_bytes  \\\n",
       "0 -0.293467      -0.476159 -2.046509 -0.811815   1.012501  -0.900230   \n",
       "1 -0.293467       2.085784 -1.269036 -0.811815   0.654220  -0.900230   \n",
       "2 -0.293467      -0.476159 -0.787101  1.007423  -1.119311  -0.900230   \n",
       "3 -0.293467      -0.476159 -0.448643 -0.811815   0.793761   1.454865   \n",
       "4 -0.293467      -0.476159 -0.448643 -0.811815   0.747902   0.968808   \n",
       "\n",
       "       land  wrong_fragment    urgent       hot             ...               \\\n",
       "0 -0.014089       -0.093425 -0.008453 -0.147181             ...                \n",
       "1 -0.014089       -0.093425 -0.008453 -0.147181             ...                \n",
       "2 -0.014089       -0.093425 -0.008453 -0.147181             ...                \n",
       "3 -0.014089       -0.093425 -0.008453 -0.147181             ...                \n",
       "4 -0.014089       -0.093425 -0.008453 -0.147181             ...                \n",
       "\n",
       "   rerror_rate  same_srv_rate  diff_srv_rate  srv_diff_host_rate  \\\n",
       "0    -0.381289       0.778461      -0.746146           -0.496460   \n",
       "1    -0.381289      -1.320214       1.887607           -0.496460   \n",
       "2    -0.381289      -1.356394       1.068041           -0.496460   \n",
       "3    -0.381289       0.778461      -0.746146           -0.496460   \n",
       "4    -0.381289       0.778461      -0.746146            1.307375   \n",
       "\n",
       "   dst_host_count  dst_host_srv_count  dst_host_same_srv_rate  \\\n",
       "0       -0.309308           -0.479823               -0.728866   \n",
       "1        0.732270           -1.589673               -1.203503   \n",
       "2        0.732270           -0.459048               -0.917737   \n",
       "3       -1.531294            1.096817                1.049686   \n",
       "4        0.732270            1.096817                1.049686   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0               -0.106055                     1.249816   \n",
       "1                2.308407                     2.014334   \n",
       "2                0.332260                    -0.670457   \n",
       "3               -0.973971                    -0.108002   \n",
       "4               -0.973971                    -0.670457   \n",
       "\n",
       "   dst_host_srv_diff_host_rate  \n",
       "0                    -0.591798  \n",
       "1                    -0.591798  \n",
       "2                    -0.591798  \n",
       "3                     1.446408  \n",
       "4                    -0.591798  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_power_trans = pd.DataFrame(data=power_transform(X=data.values,method=\"yeo-johnson\"),columns=list(data.columns))\n",
    "# data_power_trans.describe()\n",
    "data_power_trans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1400x1800 with 34 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_data_features(X = data_power_trans, data_cols=data_cols, y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dump new transformed dataset in pickle file\n",
    "data_power_trans[\"Class\"] = y_train\n",
    "with open('../Data/NSL-KDD/data_power_trans.pickle', 'wb') as f:\n",
    "    pickle.dump(data_power_trans,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>rerror_rate</th>\n",
       "      <th>same_srv_rate</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>-0.443466</td>\n",
       "      <td>-0.792078</td>\n",
       "      <td>-0.606907</td>\n",
       "      <td>-0.007679</td>\n",
       "      <td>-0.004919</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.374362</td>\n",
       "      <td>0.771283</td>\n",
       "      <td>-0.349683</td>\n",
       "      <td>-0.374560</td>\n",
       "      <td>-0.324063</td>\n",
       "      <td>-0.818890</td>\n",
       "      <td>-0.782367</td>\n",
       "      <td>-0.280282</td>\n",
       "      <td>0.069972</td>\n",
       "      <td>-0.289103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>1.325807</td>\n",
       "      <td>-0.713710</td>\n",
       "      <td>-0.606907</td>\n",
       "      <td>-0.007737</td>\n",
       "      <td>-0.004919</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.374362</td>\n",
       "      <td>-1.321428</td>\n",
       "      <td>0.482201</td>\n",
       "      <td>-0.374560</td>\n",
       "      <td>0.734343</td>\n",
       "      <td>-1.035688</td>\n",
       "      <td>-1.161030</td>\n",
       "      <td>2.736852</td>\n",
       "      <td>2.367737</td>\n",
       "      <td>-0.289103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>-0.443466</td>\n",
       "      <td>-0.635342</td>\n",
       "      <td>0.368519</td>\n",
       "      <td>-0.007762</td>\n",
       "      <td>-0.004919</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.374362</td>\n",
       "      <td>-1.389669</td>\n",
       "      <td>0.038529</td>\n",
       "      <td>-0.374560</td>\n",
       "      <td>0.734343</td>\n",
       "      <td>-0.809857</td>\n",
       "      <td>-0.938287</td>\n",
       "      <td>-0.174417</td>\n",
       "      <td>-0.480197</td>\n",
       "      <td>-0.289103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>-0.443466</td>\n",
       "      <td>-0.556973</td>\n",
       "      <td>-0.606907</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>-0.002891</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.374362</td>\n",
       "      <td>0.771283</td>\n",
       "      <td>-0.349683</td>\n",
       "      <td>-0.374560</td>\n",
       "      <td>-1.533670</td>\n",
       "      <td>1.258754</td>\n",
       "      <td>1.066401</td>\n",
       "      <td>-0.439078</td>\n",
       "      <td>-0.383108</td>\n",
       "      <td>0.066252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>-0.443466</td>\n",
       "      <td>-0.556973</td>\n",
       "      <td>-0.606907</td>\n",
       "      <td>-0.007728</td>\n",
       "      <td>-0.004814</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.374362</td>\n",
       "      <td>0.771283</td>\n",
       "      <td>-0.349683</td>\n",
       "      <td>-0.028179</td>\n",
       "      <td>0.734343</td>\n",
       "      <td>1.258754</td>\n",
       "      <td>1.066401</td>\n",
       "      <td>-0.439078</td>\n",
       "      <td>-0.480197</td>\n",
       "      <td>-0.289103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  protocol_type   service      flag  src_bytes  dst_bytes  \\\n",
       "0 -0.110249      -0.443466 -0.792078 -0.606907  -0.007679  -0.004919   \n",
       "1 -0.110249       1.325807 -0.713710 -0.606907  -0.007737  -0.004919   \n",
       "2 -0.110249      -0.443466 -0.635342  0.368519  -0.007762  -0.004919   \n",
       "3 -0.110249      -0.443466 -0.556973 -0.606907  -0.007723  -0.002891   \n",
       "4 -0.110249      -0.443466 -0.556973 -0.606907  -0.007728  -0.004814   \n",
       "\n",
       "       land  wrong_fragment    urgent       hot             ...               \\\n",
       "0 -0.014089       -0.089486 -0.007736 -0.095076             ...                \n",
       "1 -0.014089       -0.089486 -0.007736 -0.095076             ...                \n",
       "2 -0.014089       -0.089486 -0.007736 -0.095076             ...                \n",
       "3 -0.014089       -0.089486 -0.007736 -0.095076             ...                \n",
       "4 -0.014089       -0.089486 -0.007736 -0.095076             ...                \n",
       "\n",
       "   rerror_rate  same_srv_rate  diff_srv_rate  srv_diff_host_rate  \\\n",
       "0    -0.374362       0.771283      -0.349683           -0.374560   \n",
       "1    -0.374362      -1.321428       0.482201           -0.374560   \n",
       "2    -0.374362      -1.389669       0.038529           -0.374560   \n",
       "3    -0.374362       0.771283      -0.349683           -0.374560   \n",
       "4    -0.374362       0.771283      -0.349683           -0.028179   \n",
       "\n",
       "   dst_host_count  dst_host_srv_count  dst_host_same_srv_rate  \\\n",
       "0       -0.324063           -0.818890               -0.782367   \n",
       "1        0.734343           -1.035688               -1.161030   \n",
       "2        0.734343           -0.809857               -0.938287   \n",
       "3       -1.533670            1.258754                1.066401   \n",
       "4        0.734343            1.258754                1.066401   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0               -0.280282                     0.069972   \n",
       "1                2.736852                     2.367737   \n",
       "2               -0.174417                    -0.480197   \n",
       "3               -0.439078                    -0.383108   \n",
       "4               -0.439078                    -0.480197   \n",
       "\n",
       "   dst_host_srv_diff_host_rate  \n",
       "0                    -0.289103  \n",
       "1                    -0.289103  \n",
       "2                    -0.289103  \n",
       "3                     0.066252  \n",
       "4                    -0.289103  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_std = StandardScaler().fit_transform(data.values)\n",
    "data_std = pd.DataFrame(data=data_std,columns=list(data.columns))\n",
    "# data_std.describe()\n",
    "data_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1400x1800 with 34 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_data_features(X= data_std, data_cols = data_cols, y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dump new scaled data to pickle file\n",
    "data_std[\"Class\"] = y_train \n",
    "with open('../Data/NSL-KDD/data_std.pickle', 'wb') as f:\n",
    "    pickle.dump(data_std,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dos': 0, 'normal': 1, 'probe': 2, 'r2l': 3, 'u2r': 4}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
